numpy.random.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral F distribution. Samples

numpy.random.rand(d0, d1, ..., dn) Random values in a given shape. Create an array of the given shape and populate

numpy.polynomial.chebyshev.chebvander3d(x, y, z, deg)

numpy.polynomial.polynomial.polygrid2d(x, y, c)

RandomState.exponential(scale=1.0, size=None) Draw samples from an exponential distribution. Its

classmethod Chebyshev.basis(deg, domain=None, window=None)

classmethod Chebyshev.fit(x, y, deg, domain=None, rcond=None, full=False, w=None, window=None)

dtype.subdtype Tuple (item_dtype, shape) if this

numpy.setdiff1d(ar1, ar2, assume_unique=False)

numpy.less_equal(x1, x2[, out]) = Return the truth value of (x1 =< x2) element-wise.